// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H

namespace Eigen {

/** \class TensorLayoutSwap
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Swap the layout from col-major to row-major, or row-major
  * to col-major, and invert the order of the dimensions.
  *
  * Beware: the dimensions are reversed by this operation. If you want to
  * preserve the ordering of the dimensions, you need to combine this
  * operation with a shuffle.
  *
  * \example:
  * Tensor<float, 2, ColMajor> input(2, 4);
  * Tensor<float, 2, RowMajor> output = input.swap_layout();
  * eigen_assert(output.dimension(0) == 4);
  * eigen_assert(output.dimension(1) == 2);
  *
  * array<int, 2> shuffle(1, 0);
  * output = input.swap_layout().shuffle(shuffle);
  * eigen_assert(output.dimension(0) == 2);
  * eigen_assert(output.dimension(1) == 4);
  *
  */
namespace internal {
    template <typename XprType> struct traits<TensorLayoutSwapOp<XprType>> : public traits<XprType>
    {
        typedef typename XprType::Scalar Scalar;
        typedef traits<XprType> XprTraits;
        typedef typename XprTraits::StorageKind StorageKind;
        typedef typename XprTraits::Index Index;
        typedef typename XprType::Nested Nested;
        typedef typename remove_reference<Nested>::type _Nested;
        static const int NumDimensions = traits<XprType>::NumDimensions;
        static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
        typedef typename XprTraits::PointerType PointerType;
    };

    template <typename XprType> struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
    {
        typedef const TensorLayoutSwapOp<XprType>& type;
    };

    template <typename XprType> struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType>>::type>
    {
        typedef TensorLayoutSwapOp<XprType> type;
    };

}  // end namespace internal

template <typename XprType> class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
{
public:
    typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
    typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr) : m_xpr(expr) {}

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
protected:
    typename XprType::Nested m_xpr;
};

// Eval as rvalue
template <typename ArgType, typename Device> struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
    typedef TensorLayoutSwapOp<ArgType> XprType;
    typedef typename XprType::Index Index;
    static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
    typedef DSizes<Index, NumDims> Dimensions;

    enum
    {
        IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
        PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
        Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
        CoordAccess = false,  // to be implemented
        RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device)
    {
        for (int i = 0; i < NumDims; ++i) { m_dimensions[i] = m_impl.dimensions()[NumDims - 1 - i]; }
    }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_impl.bind(cgh); }
#endif

    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { return m_impl.evalSubExprsIfNeeded(data); }
    EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_impl.coeff(index); }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { return m_impl.template packet<LoadMode>(index); }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { return m_impl.costPerCoeff(vectorized); }

    EIGEN_DEVICE_FUNC typename Storage::Type data() const { return constCast(m_impl.data()); }

    const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }

protected:
    TensorEvaluator<ArgType, Device> m_impl;
    Dimensions m_dimensions;
};

// Eval as lvalue
template <typename ArgType, typename Device>
struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device> : public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
    typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
    typedef TensorLayoutSwapOp<ArgType> XprType;

    enum
    {
        IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
        PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
        Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
        CoordAccess = false  // to be implemented
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) { return this->m_impl.coeffRef(index); }
    template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x)
    {
        this->m_impl.template writePacket<StoreMode>(index, x);
    }
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
